index.rst

Learning to rank

.. toctree::
   :maxdepth: 1

   pointwise
   pairwise
   batchwise
   distillation
   generative
   mlm
   generation
   alignment

Learning to rank is handled by various classes. Some are located in the :ref:`learning module <Learning>`.

Listeners

.. autoxpmconfig:: xpmir.letor.learner.ValidationListener

Scorers

Scorers are able to give a score to a (query, document) pair. Among the scorers, some are have learnable parameters.

.. autoxpmconfig:: xpmir.rankers.Scorer
   :members: initialize, rsv, to, eval, getRetriever

.. autoxpmconfig:: xpmir.rankers.RandomScorer

.. autoxpmconfig:: xpmir.rankers.AbstractModuleScorer

.. autoxpmconfig:: xpmir.rankers.LearnableScorer

Adapters

.. autoxpmconfig:: xpmir.rankers.adapters.ScorerTransformAdapter

Utility functions

.. autofunction:: xpmir.rankers.scorer_retriever

Retrievers

Scores can be used as retrievers through a :py:class:`xpmir.rankers.TwoStageRetriever`

Samplers

.. currentmodule:: xpmir.letor.samplers

Samplers provide samples in the form of records. They all inherit from:

.. autoclass:: SerializableIterator

.. autoxpmconfig:: ModelBasedSampler

Records for training

.. automodule:: xpmir.letor.records
   :members: PointwiseRecord, PairwiseRecord

Document samplers

Useful for pre-training or when learning index parameters (e.g. for FAISS).

.. currentmodule:: xpmir.documents.samplers

.. autoxpmconfig:: DocumentSampler

.. autoxpmconfig:: HeadDocumentSampler

.. autoxpmconfig:: RandomDocumentSampler

Adapters

.. autoxpmconfig:: xpmir.letor.samplers.hydrators.SampleTransform

.. autoxpmconfig:: xpmir.letor.samplers.hydrators.SampleHydrator

.. autoxpmconfig:: xpmir.letor.samplers.hydrators.SamplePrefixAdding

.. autoxpmconfig:: xpmir.letor.samplers.hydrators.SampleTransformList